Advanced Predictive Maintenance and Condition Monitoring Techniques

Condition Monitoring Fundamentals

• Overview of condition monitoring and predictive maintenance
• Types of equipment and systems suitable for condition monitoring
• Key parameters to monitor: vibration, temperature, pressure, and wear
• Benefits of predictive maintenance in reducing downtime and costs

Advanced Predictive Maintenance Technologies

• Vibration analysis techniques and tools
• Acoustic emission monitoring and its applications
• Infrared thermography for predictive maintenance
• Oil analysis and particle counting for machinery health monitoring

Data Analytics and Fault Detection

• Data collection methods for predictive maintenance
• Statistical and machine learning techniques for fault detection
• Predictive models: Regression analysis, neural networks, and decision trees
• Case studies on successful predictive maintenance applications

Implementation and Optimization of Predictive Maintenance

• Steps for implementing a predictive maintenance program
• Integrating condition monitoring data with maintenance management systems
• Optimizing maintenance schedules and resource allocation
• Evaluating the ROI of predictive maintenance strategies

1ST BATCH: Tuesday, April 28, 2026 — Friday, May 1, 2026.

2ND BATCH: Tuesday, August 18, 2026 — Friday, August 21, 2026.

3RD BATCH: Tuesday, December 15, 2026 — Friday, December 18, 2026.

The training methodology integrates lectures, interactive discussions, collaborative group exercises, and
illustrative examples. Participants will acquire a blend of theoretical insights and hands-on practical
experience, emphasizing the application of learned techniques. This approach ensures that attendees return
to their professional environments equipped with both the competence and self-assurance to effectively
implement the acquired skills in their responsibilities.